9781484265482 - Beginning MLOps with MLFlow: Deploy Models in AWS SageMaker, Google Cloud, and Microsoft Azure de Alla, Sridhar; Adari, Suman Kalyan (28 resultados)

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Paperback. Condición: new. Paperback. Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure. This book guides you through the process of data analysis, model construction, and training.The authors begin by introducing you to basic data analysis on a credit card data set and teach you how to analyze the features and their relationships to the target variable. You will learn how to build logistic regression models in scikit-learn and PySpark, and you will go through the process of hyperparameter tuning with a validation data set. You will explore three different deployment setups of machine learning models with varying levels of automation to help you better understand MLOps. MLFlow is covered and you will explore how to integrate MLOps into your existing code, allowing you to easily track metrics, parameters, graphs, and models. You will be guided through the process of deploying and querying your models with AWS SageMaker, Google Cloud, and Microsoft Azure. And you will learn how to integrate your MLOps setups using Databricks. What You Will LearnPerform basic data analysis and construct models in scikit-learn and PySparkTrain, test, and validate your models (hyperparameter tuning)Know what MLOps is and what an ideal MLOps setup looks likeEasily integrate MLFlow into your existing or future projectsDeploy your models and perform predictions with them on the cloudWho This Book Is ForData scientists and machine learning engineers who want to learn MLOps and know how to operationalize their models Intermediate-Advanced user level Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Condición: new. Paperback. Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure. This book guides you through the process of data analysis, model construction, and training.The authors begin by introducing you to basic data analysis on a credit card data set and teach you how to analyze the features and their relationships to the target variable. You will learn how to build logistic regression models in scikit-learn and PySpark, and you will go through the process of hyperparameter tuning with a validation data set. You will explore three different deployment setups of machine learning models with varying levels of automation to help you better understand MLOps. MLFlow is covered and you will explore how to integrate MLOps into your existing code, allowing you to easily track metrics, parameters, graphs, and models. You will be guided through the process of deploying and querying your models with AWS SageMaker, Google Cloud, and Microsoft Azure. And you will learn how to integrate your MLOps setups using Databricks. What You Will LearnPerform basic data analysis and construct models in scikit-learn and PySparkTrain, test, and validate your models (hyperparameter tuning)Know what MLOps is and what an ideal MLOps setup looks likeEasily integrate MLFlow into your existing or future projectsDeploy your models and perform predictions with them on the cloudWho This Book Is ForData scientists and machine learning engineers who want to learn MLOps and know how to operationalize their models Intermediate-Advanced user level Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Condición: Neu. Beginning MLOps with MLFlow | Deploy Models in AWS SageMaker, Google Cloud, and Microsoft Azure | Sridhar Alla (u. a.) | Taschenbuch | xiv | Englisch | 2020 | Apress | EAN 9781484265482 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure. This book guides you through the process of data analysis, model construction, and training.The authors begin by introducing you to basic data analysis on a credit card data set and teach you how to analyze the features and their relationships to the target variable. You will learn how to build logistic regression models in scikit-learn and PySpark, and you will go through the process of hyperparameter tuning with a validation data set. You will explore three different deployment setups of machine learning models with varying levels of automation to help you better understand MLOps. MLFlow is covered and you will explore how to integrate MLOps into your existing code, allowing you to easily track metrics, parameters, graphs, and models. You will be guided through the process of deploying and querying your models with AWS SageMaker, Google Cloud, and Microsoft Azure. And you will learn how to integrate your MLOps setups using Databricks. What You Will LearnPerform basic data analysis and construct models in scikit-learn and PySparkTrain, test, and validate your models (hyperparameter tuning)Know what MLOps is and what an ideal MLOps setup looks likeEasily integrate MLFlow into your existing or future projectsDeploy your models and perform predictions with them on the cloudWho This Book Is ForData scientists and machine learning engineers who want to learn MLOps and know how to operationalize their models 344 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Intermediate-Advanced user level|Covers the concepts behind MLOps that you need to know to operationalize your machine learning solutions for practical useShows you how to deploy models with AWS SageMaker, Google Cloud, and Microsoft Azure.

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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure. ¿This book guides you through the process of data analysis, model construction, and training.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 344 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure. This book guides you through the process of data analysis, model construction, and training.The authors begin by introducing you to basic data analysis on a credit card data set and teach you how to analyze the features and their relationships to the target variable. You will learn how to build logistic regression models in scikit-learn and PySpark, and you will go through the process of hyperparameter tuning with a validation data set. You will explore three different deployment setups of machine learning models with varying levels of automation to help you better understand MLOps. MLFlow is covered and you will explore how to integrate MLOps into your existing code, allowing you to easily track metrics, parameters, graphs, and models. You will be guided through the process of deploying and querying your models with AWS SageMaker, Google Cloud, and Microsoft Azure. And you will learn how to integrate your MLOps setups using Databricks. What You Will LearnPerform basic data analysis and construct models in scikit-learn and PySparkTrain, test, and validate your models (hyperparameter tuning)Know what MLOps is and what an ideal MLOps setup looks likeEasily integrate MLFlow into your existing or future projectsDeploy your models and perform predictions with them on the cloudWho This Book Is ForData scientists and machine learning engineers who want to learn MLOps and know how to operationalize their models.